Instrumenting Python GIL with eBPF
Python's Global Interpreter Lock (GIL) simplifies memory management but limits performance in multi-threaded, CPU-bound programs. Coroot explains GIL impact and measurement using eBPF, offering insights and automation in version 1.3.1 for Python users.
Read original article
Python's Global Interpreter Lock (GIL) simplifies memory management but limits performance in multi-threaded, CPU-bound programs. Coroot explains the impact of the GIL and how to measure it using eBPF. By instrumenting the take_gil function, they track lock acquisition time. Even in stripped Python interpreters, they locate pthread_cond_timedwait for monitoring. Their eBPF programs record start and end times, providing insights into GIL latency. Coroot's agent automates Python app instrumentation, revealing up to 36ms lock time per second. Understanding GIL impact is crucial before considering language changes. Coroot offers GIL monitoring in version 1.3.1, emphasizing measurement before action. Users can install Coroot Community Edition for Python GIL insights without altering code.
Related
Coverage at a Crossroads
Coverage.py is evolving to reduce execution-time overhead by adopting SlipCover's low-overhead approach for code coverage. Python 3.12's sys.monitoring improves line coverage, but challenges remain for branch coverage. SlipCover's method shows promise, requiring adjustments for optimal results.
What's up Python? Django get background tasks, a new REPL, bye bye gunicorn
Several Python updates include Django's background task integration, a new lightweight Python REPL, Uvicorn's multiprocessing support, and PyPI blocking outlook.com emails to combat bot registrations, enhancing Python development and security.
How eBPF is shaping the future of Linux and platform engineering
eBPF, developed by Daniel Borkmann, revolutionizes Linux by enabling custom programs in the kernel. It enhances networking, security, and observability, bridging monolithic and microkernel architectures for improved performance and flexibility.
Show HN: Xcapture-BPF – like Linux top, but with Xray vision
0x.tools simplifies Linux application performance analysis without requiring upgrades or heavy frameworks. It offers thread monitoring, CPU usage tracking, system call analysis, and kernel wait location identification. The xcapture-bpf tool enhances performance data visualization through eBPF. Installation guides are available for RHEL 8.1 and Ubuntu 24.04.
Dynolog: Open-Source System Observability
Dynolog is an open-source observability tool for optimizing AI applications on distributed CPU-GPU systems. It offers continuous monitoring of performance metrics, integrates with PyTorch Profiler and Kineto CUDA profiling library, and supports GPU monitoring for NVIDIA GPUs and CPU events for Intel and AMD CPUs. Developed in Rust, Dynolog focuses on Linux platforms to enhance AI model observability in cloud environments.
1 commentsRelated
Coverage at a Crossroads
Coverage.py is evolving to reduce execution-time overhead by adopting SlipCover's low-overhead approach for code coverage. Python 3.12's sys.monitoring improves line coverage, but challenges remain for branch coverage. SlipCover's method shows promise, requiring adjustments for optimal results.
What's up Python? Django get background tasks, a new REPL, bye bye gunicorn
Several Python updates include Django's background task integration, a new lightweight Python REPL, Uvicorn's multiprocessing support, and PyPI blocking outlook.com emails to combat bot registrations, enhancing Python development and security.
How eBPF is shaping the future of Linux and platform engineering
eBPF, developed by Daniel Borkmann, revolutionizes Linux by enabling custom programs in the kernel. It enhances networking, security, and observability, bridging monolithic and microkernel architectures for improved performance and flexibility.
Show HN: Xcapture-BPF – like Linux top, but with Xray vision
0x.tools simplifies Linux application performance analysis without requiring upgrades or heavy frameworks. It offers thread monitoring, CPU usage tracking, system call analysis, and kernel wait location identification. The xcapture-bpf tool enhances performance data visualization through eBPF. Installation guides are available for RHEL 8.1 and Ubuntu 24.04.
Dynolog: Open-Source System Observability
Dynolog is an open-source observability tool for optimizing AI applications on distributed CPU-GPU systems. It offers continuous monitoring of performance metrics, integrates with PyTorch Profiler and Kineto CUDA profiling library, and supports GPU monitoring for NVIDIA GPUs and CPU events for Intel and AMD CPUs. Developed in Rust, Dynolog focuses on Linux platforms to enhance AI model observability in cloud environments.